VIEWS

Visual Variables

mapping data features to visual representations

View by Gapminder

View by Gapminder

When you visualize, then you materialize…
— Denis Waitley


The process of mapping data to visual variables is called visual mapping. The mapping process represents your decisions to encode a data feature to a set of visual characteristics.

Grammar of Graphics

the language for descibing visual representations

If we consider a data representation as an instrument for communication, it leads us to consider the language we can use to maximize understanding. The language for information visualization is referred to as the Grammar of Graphics. This grammar provides a form of describing the visual architecture, structure, marks, and properties of a view. Analogous to a painters canvas, we can use the language to describe a graphic frame and layers of visual features that will form the painting.

Visual Variables are a specified set of modifications that can be applied to objects in order to encode information. Although there are varying schools of thought for the concept, we will focus on four simple characteristics that can be used to describe and define virtually any visualization:

  • position : where is it located within a coordinate space
  • shape : what form does it have
  • size : how small or big is it
  • color : what color is it

The primary objective of visualization mapping is to identify a set of visualization primitives that can effectively convey the informational content of data. The choice of the variable, which would be most appropriate to represent each aspect of information depends on its characteristics.

Knowing the characteristics of data relevant for visualization mapping is the fi rst important step in designing an eff ective data visualization technique. The most primlnve components of visualization techniques are the marks (graphical symbols) which constitute the graphics. I

Gap Minder

Let’s make some bubbles

# file path parameter
gapminder_file_path <- "./archetypes/gapminder/gapminder.csv"
gapminder <- read_csv(gapminder_file_path)

# Get continent reference
gapminder$continent <- countrycode(gapminder$country_code, origin = 'iso3c', destination = 'continent')
gapminder

Visual Mapping

Location, income by life expectancy

Shape, circle, country

Size, population

Color, continent

Labels

Final Result

References

citations for narrative and data sources

  • Narrative: Free material from www.gapminder.org